A Spatial-Temporal Feature-Based Detection Framework for Infrared Dim Small Target

Du, Jinming; Lü, Huanzhang; Zhang, Luping; Hu, Moufa; Chen, Sheng; Deng, Yingjie; Shen, Xinglin; Zhang, Yunlong

doi:10.1109/tgrs.2021.3117131

Cited by 55 publications

(40 citation statements)

References 35 publications

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“…To eliminate the influence of static noise in an infrared image sequence on the detection of small infrared objects, Yao et al [16] proposed an optimized FCOS network model that uses traditional filtering methods and spatialtemporal feature fusion to preprocess sample images. Du et al [17] proposed an interframe energy accumulation (IFEA) enhancement mechanism to effectively extract spatial-temporal information in the infrared sequence. The method is specially designed to suppress strong spatially nonstationary clutter, enhance the object, and improve accuracy.…”

Section: Infrared Small-object Detection Methods Based On Cnnmentioning

confidence: 99%

“…Many researchers have been inspired by small-object detection methods and have proposed detection models suitable for small objects. The optimized methods of these models can be categorized into spatial-temporal information fusion [15][16][17], residual/background information prediction [18,19], optimized region proposal [20,21], and multiscale information fusion [22][23][24][25]. The spatial-temporal information fusion method reduces static noise by combining adjacent frames in an infrared image sequence.…”

Section: Introductionmentioning

confidence: 99%

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IRSDet: Infrared Small-Object Detection Network Based on Sparse-Skip Connection and Guide Maps

2022

Electronics

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Detecting small objects in infrared images remains a challenge because most of them lack shape and texture. In this study, we proposed an infrared small-object detection method to improve the capacity for detecting thermal objects in complex scenarios. First, a sparse-skip connection block is proposed to enhance the response of small infrared objects and suppress the background response. This block is used to construct the detection model backbone. Second, a region attention module is designed to emphasize the features of infrared small objects and suppress background regions. Finally, a batch-averaged biased classification loss function is designed to improve the accuracy of the detection model. The experimental results show that the proposed small-object detection framework significantly increases precision, recall, and F1-score, showing that, compared with the current advanced detection models for small-object detection, the proposed detection framework has better performance in infrared small-object detection under complex backgrounds. The insights gained from this study may provide new ideas for infrared small object detection and tracking.

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Section: Infrared Small-object Detection Methods Based On Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IRSDet: Infrared Small-Object Detection Network Based on Sparse-Skip Connection and Guide Maps

2022

Electronics

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“…The data-driven CNN is able to learn features adaptively from images and outperforms model-driven methods for the detection of infrared small targets. According to different processing paradigms, CNN-based methods for SIRST detection can be divided into detection-based [19][20][21][22] and segmentation-based methods [12,13,[23][24][25][26][27][28][29]. The detectionbased method outputs the position and scale information of targets directly for the input image, in the same way as generic target detection algorithms, such as Faster RCNN [30] and SSD [31].…”

Section: Detection-based Infrared Small Target Detectionmentioning

confidence: 99%

“…ISDet [19] trains both the image filtering network and the target detection network in an end-to-end manner. Du et al follows the two-stage paradigm of Faster RCNN and designs the small-iou strategy for positive and negative sample partitioning to solve the problem of false convergence and sample misjudgment due to small target size [20]. SSD-ST [21] drops low-resolution layers and enhances high-resolution layers of SSD to adapt the detection of infrared small target.…”

Section: Detection-based Infrared Small Target Detectionmentioning

confidence: 99%

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

Chen

Tan

2022

Remote Sensing

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Infrared small target detection occupies an important position in the infrared search and track system. The most common size of infrared images has developed to 640×512. The field-of-view (FOV) also increases significantly. As the result, there is more interference that hinders the detection of small targets in the image. However, the traditional model-driven methods do not have the capability of feature learning, resulting in poor adaptability to various scenes. Owing to the locality of convolution kernels, recent convolutional neural networks (CNN) cannot model the long-range dependency in the image to suppress false alarms. In this paper, we propose a hierarchical vision transformer-based method for infrared small target detection in larger size and FOV images of 640×512. Specifically, we design a hierarchical overlapped small patch transformer (HOSPT), instead of the CNN, to encode multi-scale features from the single-frame image. For the decoder, a top-down feature aggregation module (TFAM) is adopted to fuse features from adjacent scales. Furthermore, after analyzing existing loss functions, a simple yet effective combination is exploited to optimize the network convergence. Compared to other state-of-the-art methods, the normalized intersection-over-union (nIoU) on our IRST640 dataset and public SIRST dataset reaches 0.856 and 0.758. The detailed ablation experiments are conducted to validate the effectiveness and reasonability of each component in the method.

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“…In the past few decades, a large number of approaches have been proposed to improve the performance of infrared small target detection methods [4] [5] [9] [10] [11] [12]. Generally speaking, they can be simply categorized into two groups: single frame-based methods, multi-frame-based methods.…”

Section: Introductionmentioning

confidence: 99%

IISTD: Image Inpainting-Based Small Target Detection in a Single Infrared Image

Ling

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Small target detection is a crucial and challenging task in infrared search and track system. Background estimationbased methods is an effective and important approach for infrared small target detection. Affected by the target pixels, existing background estimation methods may reconstruct an inaccurate background. Based on image inpainting technique, we propose a novel two-stage approach to predict more accurate backgrounds. At the first stage, the inner and outer windowbased image inpainting (IOWII) is used to obtain a rough background estimation. Then a mask of candidate target region is automatically obtained by calculating and evaluating the difference between raw image and rough background. In the second stage, the final accurate background is predicted by maskbased image inpainting (MII). It recovers the removed candidate target area using the information of surrounding background pixels, avoiding target pixels to participate in the calculation of background reconstruction. Finally, the target saliency map is obtained by subtracting the final estimated background from the original image, and a simple adaptive threshold is used to segment the target. Experimental results on real infrared images and sequences demonstrate that the proposed method outperforms other state-of-the-arts. It is simple and effective, with strong robustness and good real-time performance.

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A Spatial-Temporal Feature-Based Detection Framework for Infrared Dim Small Target

Cited by 55 publications

References 35 publications

IRSDet: Infrared Small-Object Detection Network Based on Sparse-Skip Connection and Guide Maps

IRSDet: Infrared Small-Object Detection Network Based on Sparse-Skip Connection and Guide Maps

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

IISTD: Image Inpainting-Based Small Target Detection in a Single Infrared Image

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